METABOLIC NETWORK ANALYSIS OF BIOCHEMCIAL TRAJECTORIES IN ALZHEIMER'S DISEASE ABSTRACT Despite advances, clinical trials have not yielded therapies to prevent or slow progression of Alzheimer's Disease (AD) with recent failures highlighting our incomplete knowledge of disease mechanisms. Accumulating evidence suggests the synaptic failure in AD is associated with dysregulation in multiple networks and that AD is not a singular condition but may be a combination of altered networks calling for a systems approach. AD susceptibility is likely influenced by many different common and rare genomic variants spread across hundreds of genes. Such genetic heterogeneity poses enormous challenges in defining disease mechanisms and approaches for drug development. Increasing evidence supports that AD is a metabolic disease with diabetes co-morbidity and a range of metabolic perturbations occurring early in disease. APOE4 is the strongest genetic risk factor for AD. APOE functions in lipid metabolism and presence of the APOE4 variant is correlated with higher cholesterol levels in the blood, suggesting again an important role for metabolism in AD. In addition, most of the genes that have recently been implicated in AD suggest a role for lipid processing, immune function regulation and phagocytosis that are all related to metabolic functions. Yet, a detailed mapping of interconnected metabolic networks that fail in AD are not defined. Metabolomics allows simultaneous measurement of 100's to 1000's of metabolites for mapping perturbations into metabolic networks, enabling a systems approach to the study of AD. The metabolome captures net influences of the genome, gut microbiome and environment in AD. Metabolomics data provides a functional readout of effects of genetic variants on metabolism, reducing the complexity of genetics to common effects on metabolic pathways that are implicated in disease. As part of NIA's large initiatives AMP-AD and MOVE-AD, we have established the AD Metabolomics Consortium (ADMC) aiming at building a comprehensive metabolomics database for AD for interrogation of global metabolic failures in disease and to define metabolic pathways that are implicated in cognitive decline. By leveraging large NIH investments in the AD Neuroimaging Initiative (ADNI), ADMC, AMP- AD and MOVE-AD and by adding experts in metabolic reconstruction and modeling we will frame the metabolic basis for AD progression. In Aim 1, we propose to profile longitudinal samples from ADNI cohorts where deep phenotype data exists that enables us to track early biochemical changes related to both CSF amyloid-beta and tau pathology and across the trajectory of disease. Through imaging data we will link peripheral metabolic changes to changes in the brain. In Aim 2, we propose to create an integrated molecular Atlas for AD. This resource for the AD community will connect genotypes and metabotypes, providing a functional readout of genetic variants implicated in AD. In Aim 3, we propose to build the first genome-scale model of metabolic changes underlying AD progression. Together, this effort will provide a biochemical roadmap towards effective early interventions for AD.